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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Evolving a Team of Asymmetric Predator Agents That Do Not Compute in Predator-Prey Pursuit Problem
verfasst von : Ivan Tanev, Milen Georgiev, Katsunori Shimohara, Thomas Ray
Erschienen in: Artificial Intelligence: Methodology, Systems, and Applications
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Abstract
We herein revisit the predator-prey pursuit problem – using very simple predator agents. The latter – intended to model the emerging micro- and nano-robots – are morphologically simple. They feature a single line-of-sight sensor and a simple control of their two thrusters. The agents are behaviorally simple as well – their decision-making involves no computing, but rather – a direct mapping of the few perceived environmental states into the corresponding pairs of thrust values. We apply genetic algorithms to evolve such a mapping that results in the successful behavior of the team of these predator agents. To enhance the generality of the evolved behavior, we propose an asymmetric morphology of the agents – an angular offset of their sensor. Our experimental results verify that the offset of both 20° and 30° yields efficient and consistent evolution of successful behaviors of the agents in all tested initial situations.